We have used the VSM model implemented by Zhou et al. [1]} as a baseline to compare our approach. However, the baseline alone does not take into account semantic relations or contextual overlap between keywords or the positive language used among comments. To complement this gap, we introduce the graph-based TextRank model (TR) as a variant of enhancing VSM model. Furthermore, because sentiment patters were noticeable in bug reports [2]}, [3]}, [4]}, we incorporate the Semantic Analysis (SA) to enrich and complement both the VSM model and the TextRank model. Hence, this study comparatively evaluates different variants of approaches. Those are: VSM (baseline), VSM+SA (our approach with TR disabled), VSM+TR (our approach with SA disabled) and VSM+SA+TR (our complete approach, in Section ).
